Evaluation of ATM Location Placement Using the K-Means Clustering in BNI Denpasar Regional Office

Suwirya, I Putu; Candiasa, I Made; Dantes, Gede Rasben

doi:10.47709/cnahpc.v4i2.1580

Cited by 1 publication

(1 citation statement)

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“…The K-Means algorithm, a method for non-hierarchical data grouping, endeavors to divide the available data into multiple groups by identifying similar characteristics, thereby efficiently and accurately segregating data with shared attributes from those with differing ones (Tarigan et al, 2023). The K-Means method offers the advantage of being relatively straightforward to implement, capable of managing sizable datasets, and executing the process swiftly (Suwirya et al, 2022). K-Means clustering can serve as preprocessing for other classification methods because it efficiently organizes data into clusters based on similarity, aiding subsequent algorithms in discerning patterns and making accurate predictions (Usman & Stores, 2020).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Multi-Layer Perceptron Performance with K-Means Clustering

Pardede,

Ichsan,

Riyadi

2024

CNAHPC

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Machine learning plays a crucial role in identifying patterns within data, with classification being a prominent application. This study investigates the use of Multilayer Perceptron (MLP) classification models and explores preprocessing techniques, particularly K-Means clustering, to enhance model performance. Overfitting, a common challenge in MLP models, is addressed through the application of K-Means clustering to streamline data preparation and improve classification accuracy. The study begins with an overview of overfitting in MLP models, highlighting the significance of mitigating this issue. Various techniques for addressing overfitting are reviewed, including regularization, dropout, early stopping, data augmentation, and ensemble methods. Additionally, the complementary role of K-Means clustering in enhancing model performance is emphasized. Preprocessing using K-Means clustering aims to reduce data complexity and prevent overfitting in MLP models. Three datasets - Iris, Wine, and Breast Cancer Wisconsin - are employed to evaluate the performance of K-Means as a preprocessing technique. Results from cross-validation demonstrate significant improvements in accuracy, precision, recall, and F1 scores when employing K-Means clustering compared to models without preprocessing. The findings highlight the efficacy of K-Means clustering in enhancing the discriminative power of MLP classification models by organizing data into clusters based on similarity. These results have practical implications, underlining the importance of appropriate preprocessing techniques in improving classification performance. Future research could explore additional preprocessing methods and their impact on classification accuracy across diverse datasets, advancing the field of machine learning and its applications

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Section: Introductionmentioning

confidence: 99%